Accenture MIT Data Science ChallengeAbbas KeshvaniAccenture Analytics Innovation Centre, Singapore

Chicago skyline

Problem

Chicagoans expect better service

• City of Chicago provides heavy duty carts to homes

• Some homes lack a cart – new home, theft, damage

• Chicagoans request new carts by calling 311:

1. Some requests are not completed, leaving residents without refuse facilities

2. Other requests are resolved very slowly

1. Completing requests

Problem: requests are not completed

• 4757 open cases,

• 1372 have been open for a long time (120 days or more)

• Leaves residents without carts to dispose of garbage

1. Completing requests

Investigation: where are the open cases?

• Plot map of unserved requests

• Red areas have a high concentration of open cases

• Found mainly in the western interior of the city

1. Completing requests

Solution

• Improve coverage of areas in red

o Oak Park

o West Side

o Dolton

• Oak Park

• West Side

• Dolton

2. Resolving requests efficiently

Problem: resolution time is slow

• Mean time to resolve a single request shows seasonality

• Peaks in June/July and troughs in December/January

• Same June/December seasonality seen in

1. Total number of requests

2. Total time to resolve all requests

• But the magnitude of the seasonality is less in (1) than in (2), shown by shallower valleys

2. Resolving requests efficiently

Investigation: cause of slow resolution time

Number of requests Total time

• Disproportionate increase in total time, in response to increase in number of requests

• Indicated City of Chicago is operating at full capacity in summer months

• Resolution can be achieved by increasing capacity in summer by hiring more staff

2. Resolving requests efficiently

Solution

Number of requests Total time

Data

#daily aggregates for time taken to resolve

daily<-matrix(NA,16126-15001+1,1)

for(i in 15001:16126)

{

series.i<-c(garbage7[garbage7$creation==i,12])

day.i<-sum(series.i)

daily[i-15000]<-day.i

}

#daily aggregates for number of requests

no.of.req<-matrix(NA,16126-15001+1,1)

for(i in 15001:16126)

{

series.i<-nrow(garbage7[garbage7$creation==i,])

no.of.req[i-15000]<-series.i

}

#consolidate data

ts<-cbind(15001:16126,daily,no.of.req)

ts<-data.frame(ts)

ts[,4]<-as.Date(ts[,1],origin="1970-01-01")

colnames(ts)<-c("Day","Lag","Number of requests","Date")

ts[,"Mean Lag"]<-ts$Lag/ts$"Number of requests"

Map:

#get map from google maps

chicago<-get_map(location = "chicago",

zoom = 11, scale = "auto",

maptype = "terrain",

messaging = FALSE, urlonly = FALSE,

filename = "ggmapTemp", crop = TRUE,

color = c("color", "bw"),

source = c("google", "osm", "stamen", "cloudmade"),

api_key) #prepare chicago map

m<-ggmap(chicago)

m + geom_point(data=garbage3,aes(x=lon,y=lat),alpha=0) + #add points

ggtitle("Heatmap of Open cases") + #add a title

stat_binhex(bins = 60, mapping=NULL, data=trash, alpha=0.7) + #cluster data points into hexagons

scale_fill_gradient(low="blue",high="red",limits=c(0,300), na.value="red") #choose colours for binning

Plots:

#plot time taken to resolve a request

p<- ggplot(ts, aes(x=Date, y=Lag))

p + #you get an error if not for this step

geom_point(size=1.2) +

geom_smooth() +

ylim(-1000,20000) +

ggtitle("Lag to resolve a request")

#plot mean time to resolve a request

p<- ggplot(ts, aes(x=Date, y=ts[,3]))

p + #you get an error if not for this step

geom_point(size=1.2) +

ylab("Number of requests") +

geom_smooth() +

ggtitle("Mean lag to resolve a request")

#plot number of daily requests

p<- ggplot(ts, aes(x=Date, y=ts[,5]))

p + #you get an error if not for this step

geom_point(size=1.2) +

geom_smooth() +

ylab("Mean lag") +

ggtitle(“Number of requests")

R code used